Triple
T18552776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Criminal Division (Third Judicial Circuit Court of Michigan) |
E453419
|
entity |
| Predicate | caseSeverity |
P132130
|
FINISHED |
| Object | felony |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: felony | Statement: [Criminal Division (Third Judicial Circuit Court of Michigan), caseSeverity, felony]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: caseSeverity Context triple: [Criminal Division (Third Judicial Circuit Court of Michigan), caseSeverity, felony]
-
A.
lessSevereIn
Indicates that one condition, event, or factor has a lower level of severity than another within a specified context.
-
B.
typicalClinicalSeverity
Indicates the usual or characteristic level of clinical severity associated with a condition, finding, or case.
-
C.
accidentSeverity
Indicates the level or degree of seriousness associated with an accident.
-
D.
fastSeverity
Indicates the level or intensity of severity associated with something that is fast or time-critical.
-
E.
caseTypes
Indicates the types or categories of cases associated with or applicable to an entity or situation.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8d388b0c881908e610a1c45b52640 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e538027b94819082a4c4af66e170d7 |
completed | April 19, 2026, 8:16 p.m. |
| PD | Predicate disambiguation | batch_69e469e274a48190a570b25cfef4d890 |
completed | April 19, 2026, 5:36 a.m. |
| PDg | Predicate description generation | batch_69e46d2b93bc8190a6070018d7046547 |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 11:38 a.m.